Sixtine Dromigny

Gradient Variance Reveals Failure Modes in Flow-Based Generative Models

Rectified Flows learn ODE vector fields whose trajectories are straight between source and target distributions, enabling near one-step inference. We show that this straight-path objective reveals fundamental failure modes: under deterministic training, low gradient variance drives memorization of a…